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--- |
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license: odc-by |
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task_categories: |
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- text-generation |
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language: |
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- en |
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- de |
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- ja |
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- fr |
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- es |
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- it |
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- ru |
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- pt |
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- pl |
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- nl |
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- cs |
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- zh |
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- ro |
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- sv |
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- hu |
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- sk |
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- uk |
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- th |
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- da |
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- id |
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- el |
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- fi |
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- ca |
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- tr |
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- dag |
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- hr |
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- fa |
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- bg |
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- nb |
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- kiu |
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- ar |
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- vi |
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- sr |
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- ko |
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- sl |
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- lt |
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- hi |
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- he |
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- bs |
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- ms |
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- et |
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- lv |
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- bn |
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- frp |
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- is |
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- glk |
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- eu |
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- gl |
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- sq |
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- mk |
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- mr |
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- ne |
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- ka |
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- la |
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- pcm |
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- mt |
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- cy |
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- vec |
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- hy |
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- nrm |
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- wuu |
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- anp |
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- bcc |
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- ur |
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- af |
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- az |
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- ta |
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- kk |
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- nn |
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pretty_name: FinePDFs-Edu |
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size_categories: |
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- n>1T |
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configs: |
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- config_name: eng_Latn |
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default: true |
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data_files: |
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- split: train |
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path: data/eng_Latn/train/* |
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- config_name: deu_Latn |
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data_files: |
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- split: train |
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path: data/deu_Latn/train/* |
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- config_name: jpn_Jpan |
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data_files: |
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- split: train |
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path: data/jpn_Jpan/train/* |
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- config_name: fra_Latn |
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data_files: |
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- split: train |
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path: data/fra_Latn/train/* |
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- config_name: spa_Latn |
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data_files: |
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- split: train |
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path: data/spa_Latn/train/* |
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- config_name: ita_Latn |
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data_files: |
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- split: train |
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path: data/ita_Latn/train/* |
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- config_name: rus_Cyrl |
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data_files: |
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- split: train |
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path: data/rus_Cyrl/train/* |
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- config_name: por_Latn |
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data_files: |
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- split: train |
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path: data/por_Latn/train/* |
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- config_name: pol_Latn |
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data_files: |
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- split: train |
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path: data/pol_Latn/train/* |
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- config_name: nld_Latn |
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data_files: |
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- split: train |
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path: data/nld_Latn/train/* |
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- config_name: ces_Latn |
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data_files: |
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- split: train |
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path: data/ces_Latn/train/* |
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- config_name: cmn_Hani |
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data_files: |
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- split: train |
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path: data/cmn_Hani/train/* |
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- config_name: ron_Latn |
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data_files: |
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- split: train |
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path: data/ron_Latn/train/* |
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- config_name: swe_Latn |
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data_files: |
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- split: train |
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path: data/swe_Latn/train/* |
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- config_name: hun_Latn |
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data_files: |
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- split: train |
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path: data/hun_Latn/train/* |
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- config_name: slk_Latn |
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data_files: |
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- split: train |
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path: data/slk_Latn/train/* |
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- config_name: ukr_Cyrl |
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data_files: |
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- split: train |
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path: data/ukr_Cyrl/train/* |
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- config_name: tha_Thai |
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data_files: |
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- split: train |
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path: data/tha_Thai/train/* |
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- config_name: dan_Latn |
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data_files: |
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- split: train |
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path: data/dan_Latn/train/* |
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- config_name: ind_Latn |
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data_files: |
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- split: train |
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path: data/ind_Latn/train/* |
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- config_name: ell_Grek |
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data_files: |
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- split: train |
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path: data/ell_Grek/train/* |
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- config_name: fin_Latn |
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data_files: |
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- split: train |
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path: data/fin_Latn/train/* |
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- config_name: cat_Latn |
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data_files: |
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- split: train |
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path: data/cat_Latn/train/* |
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- config_name: tur_Latn |
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data_files: |
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- split: train |
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path: data/tur_Latn/train/* |
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- config_name: dag_Latn |
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data_files: |
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- split: train |
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path: data/dag_Latn/train/* |
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- config_name: hrv_Latn |
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data_files: |
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- split: train |
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path: data/hrv_Latn/train/* |
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- config_name: fas_Arab |
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data_files: |
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- split: train |
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path: data/fas_Arab/train/* |
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- config_name: bul_Cyrl |
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data_files: |
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- split: train |
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path: data/bul_Cyrl/train/* |
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- config_name: nob_Latn |
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data_files: |
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- split: train |
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path: data/nob_Latn/train/* |
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- config_name: kiu_Latn |
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data_files: |
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- split: train |
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path: data/kiu_Latn/train/* |
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- config_name: arb_Arab |
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data_files: |
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- split: train |
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path: data/arb_Arab/train/* |
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- config_name: vie_Latn |
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data_files: |
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- split: train |
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path: data/vie_Latn/train/* |
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- config_name: srp_Cyrl |
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data_files: |
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- split: train |
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path: data/srp_Cyrl/train/* |
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- config_name: kor_Hang |
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data_files: |
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- split: train |
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path: data/kor_Hang/train/* |
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- config_name: slv_Latn |
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data_files: |
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- split: train |
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path: data/slv_Latn/train/* |
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- config_name: lit_Latn |
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data_files: |
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- split: train |
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path: data/lit_Latn/train/* |
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- config_name: hin_Deva |
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data_files: |
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- split: train |
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path: data/hin_Deva/train/* |
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- config_name: heb_Hebr |
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data_files: |
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- split: train |
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path: data/heb_Hebr/train/* |
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- config_name: bos_Latn |
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data_files: |
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- split: train |
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path: data/bos_Latn/train/* |
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- config_name: zsm_Latn |
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data_files: |
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- split: train |
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path: data/zsm_Latn/train/* |
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- config_name: ekk_Latn |
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data_files: |
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- split: train |
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path: data/ekk_Latn/train/* |
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- config_name: lvs_Latn |
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data_files: |
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- split: train |
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path: data/lvs_Latn/train/* |
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- config_name: ben_Beng |
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data_files: |
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- split: train |
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path: data/ben_Beng/train/* |
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- config_name: frp_Latn |
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data_files: |
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- split: train |
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path: data/frp_Latn/train/* |
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- config_name: isl_Latn |
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data_files: |
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- split: train |
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path: data/isl_Latn/train/* |
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- config_name: glk_Arab |
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data_files: |
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- split: train |
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path: data/glk_Arab/train/* |
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- config_name: eus_Latn |
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data_files: |
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- split: train |
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path: data/eus_Latn/train/* |
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- config_name: glg_Latn |
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data_files: |
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- split: train |
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path: data/glg_Latn/train/* |
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- config_name: als_Latn |
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data_files: |
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- split: train |
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path: data/als_Latn/train/* |
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- config_name: mkd_Cyrl |
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data_files: |
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- split: train |
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path: data/mkd_Cyrl/train/* |
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- config_name: mar_Deva |
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data_files: |
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- split: train |
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path: data/mar_Deva/train/* |
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- config_name: npi_Deva |
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data_files: |
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- split: train |
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path: data/npi_Deva/train/* |
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- config_name: kat_Geor |
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data_files: |
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- split: train |
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path: data/kat_Geor/train/* |
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- config_name: lat_Latn |
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data_files: |
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- split: train |
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path: data/lat_Latn/train/* |
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- config_name: pcm_Latn |
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data_files: |
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- split: train |
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path: data/pcm_Latn/train/* |
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- config_name: mlt_Latn |
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data_files: |
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- split: train |
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path: data/mlt_Latn/train/* |
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- config_name: cym_Latn |
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data_files: |
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- split: train |
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path: data/cym_Latn/train/* |
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- config_name: vec_Latn |
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data_files: |
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- split: train |
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path: data/vec_Latn/train/* |
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- config_name: hye_Armn |
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data_files: |
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- split: train |
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path: data/hye_Armn/train/* |
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- config_name: nrm_Latn |
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data_files: |
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- split: train |
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path: data/nrm_Latn/train/* |
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- config_name: wuu_Hani |
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data_files: |
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- split: train |
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path: data/wuu_Hani/train/* |
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- config_name: anp_Deva |
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data_files: |
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- split: train |
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path: data/anp_Deva/train/* |
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- config_name: bcc_Arab |
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data_files: |
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- split: train |
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path: data/bcc_Arab/train/* |
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- config_name: urd_Arab |
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data_files: |
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- split: train |
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path: data/urd_Arab/train/* |
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- config_name: afr_Latn |
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data_files: |
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- split: train |
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path: data/afr_Latn/train/* |
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- config_name: azj_Latn |
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data_files: |
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- split: train |
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path: data/azj_Latn/train/* |
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- config_name: tam_Taml |
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data_files: |
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- split: train |
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path: data/tam_Taml/train/* |
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- config_name: kaz_Cyrl |
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data_files: |
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- split: train |
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path: data/kaz_Cyrl/train/* |
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- config_name: nno_Latn |
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data_files: |
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- split: train |
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path: data/nno_Latn/train/* |
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--- |
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# 📚 FinePDFs-Edu |
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> 350B+ of highly educational tokens from PDFs 📄 |
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## What is it? |
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📚 FinePDFs-Edu dataset consists of **350B+ tokens** of educational PDFs filtered from 📄 [FinePDFs](https://huggingface.co/datasets/HuggingFaceFW/finepdfs) dataset covering 69 languages. |
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FinePDFs was created using the formula inspired from [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu), we developed an [educational quality classifier](HuggingFaceFW/finepdfs_edu_classifier_eng_Latn) using annotations generated by Qwen3-235B-A22B-Instruct-2507 for each of 69 languages present in this dataset. |
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We then used this classifier to retain only the most educational web pages. FinePDFs-Edu outperforms FinePDFs on popular benchmarks and shows the power of classifiers trained on synthetic data. |
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The [Dataset Curation](https://huggingface.co/datasets/HuggingFaceFW/finepdfs_edu#dataset-curation) section details the process for creating the dataset. |
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While it might seem that the dataset is an order of magnitude smaller than FineWeb-Edu, unlike its web ancestor, this dataset is globally deduplicated! |
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## What is being released? |
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Along with the dataset, which includes all filtered CommonCrawl dumps since `CC-MAIN-2013-20` to `CC-MAIN-2025-08`, we also release: |
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- The [educational classifier](https://huggingface.co/HuggingFaceFW/finepdfs_edu_classifier_eng_Latn) used for the filtering (for each language) |
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- The [dataset](https://huggingface.co/datasets/HuggingFaceFW/finepdfs_eng_Latn_labeled) with educational (and 3 other) labels by Qwen3-235B-A22B-Instruct-2507 for English. |
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- The [dataset](HuggingFaceFW/finepdfs_fw_edu_labeled) with educational labels by Qwen3-235B-A22B-Instruct-2507 for 69 languages beyond English. |
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- The [code](https://github.com/huggingface/finepdfs) for training it and running inference. |
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## How to download and use 📄 FinePDFs-Edu |
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See the tables above for the `subset` of the language you want to download. |
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We currently do not provide smaller `sample` versions, but by setting `limit` or using `streaming=True` you can easily fetch a sample of the data. If there is interest from the community we might upload smaller sampled versions later on. |
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### Using 🏭 [`datatrove`](https://github.com/huggingface/datatrove/) |
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```python |
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from datatrove.pipeline.readers import ParquetReader |
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# limit determines how many documents will be streamed (remove for all) |
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# this will fetch the Portuguese filtered data |
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data_reader = ParquetReader("hf://datasets/HuggingFaceFW/finepdfs-edu/data/por_Latn/train", limit=1000) |
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for document in data_reader(): |
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# do something with document |
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print(document) |
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############################### |
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# OR for a processing pipeline: |
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############################### |
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from datatrove.executor import LocalPipelineExecutor |
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from datatrove.pipeline.readers import ParquetReader |
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from datatrove.pipeline.filters import LambdaFilter |
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from datatrove.pipeline.writers import JsonlWriter |
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pipeline_exec = LocalPipelineExecutor( |
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pipeline=[ |
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ParquetReader("hf://datasets/HuggingFaceFW/finepdfs-edu/data/por_Latn/train", limit=1000), |
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LambdaFilter(lambda doc: "hugging" in doc.text), |
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JsonlWriter("some-output-path") |
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], |
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tasks=10 |
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) |
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pipeline_exec.run() |
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``` |
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### Using `huggingface_hub` |
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```python |
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from huggingface_hub import snapshot_download |
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folder = snapshot_download( |
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"HuggingFaceFW/finepdfs-edu", |
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repo_type="dataset", |
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local_dir="./finepdfs-edu/", |
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# download the Czech filtered |
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allow_patterns=["data/ces_Latn/train/*"]) |
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``` |
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For faster downloads, make sure to install `pip install huggingface_hub[hf_transfer]` and set the environment variable `HF_HUB_ENABLE_HF_TRANSFER=1`. |
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### Using `datasets` |
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```python |
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from datasets import load_dataset |
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# get Croatian data |
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fw = load_dataset("HuggingFaceFW/finepdfs-edu", name="hrv_Latn", split="train", streaming=True) |
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``` |
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Similiar to original FinePDFs, this dataset contains high amount of language switching samples, we thus recommend using the [filtering function](https://huggingface.co/datasets/HuggingFaceFW/finepdfs#code-switching) if this is not desired. |
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## Dataset curation |
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We have used the same approach for FineWeb-Edu with minimal adjustments of the prompt. To scale to languages beyond English we decided to train separate classifier for each. |
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### Educational Scoring |
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We used [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) to score approximately 300,000 FinePDFs samples for educational quality on a 0–5 scale. The final prompt used for scoring is available [here](https://huggingface.co/HuggingFaceFW/finepdfs_edu_classifier_eng_Latn/blob/main/prompt.txt). |
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After experimenting with several prompt variants, we found that the **FineWeb-Edu** prompt yielded the most consistent and reliable results. As in FineWeb-Edu, we observed that highly technical or graduate-level content did not correlate well with the benchmarks we track. However, unlike in FineWeb-Edu, the overall average score was noticeably lower—if we had used a fixed threshold of `score = 3`, only about 2% of samples would have been retained. |
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To address this, we instead selected the **top 10%** of samples based on their education score. |
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| Threshold | Drop Rate | |
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| :-------: | :-------: | |
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| 1 | 0.3028 | |
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| 2 | 0.9451 | |
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| 3 | 0.9802 | |
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| 4 | 0.9906 | |
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| 5 | 0.9987 | |
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We also replaced the teacher model to improve multilingual coverage and take advantage of the better inference efficiency offered by Mixture-of-Experts (MoE) architectures. To identify a suitable model, we aimed for one that was most *“Claude-like”*, i.e., whose scoring behavior most closely matched **Claude Sonnet-4**. We compared models using mean squared error (MSE) on a 10k-sample development set and found that **Qwen3-235B-A22B-Instruct-2507** was both the most Claude-like and highly efficient—processing up to **14 chunks/sec on a single H100 GPU**. |
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| Model | MSE (vs. Sonnet-4) | |
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| :-------------------------------------------- | -----------------: | |
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| Qwen_Qwen3-235B-A22B-Instruct-2507 | **0.398** | |
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| Qwen_Qwen3-235B-A22B-Thinking-2507 | 0.812 | |
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| Qwen_Qwen3-30B-A3B-Instruct-2507 | 0.364 | |
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| Qwen_Qwen3-30B-A3B-Thinking-2507 | 0.925 | |
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| google_gemma-3-27b-it | 2.727 | |
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| meta-llama_Llama-3.3-70B-Instruct | 0.553 | |
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| meta-llama_Llama-4-Maverick-17B-128E-Instruct | 0.707 | |
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| meta-llama_Llama-4-Scout-17B-16E-Instruct | 1.177 | |
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| mistralai_Magistral-Small-2507 | 0.717 | |
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| zai-org_GLM-4.5-Air-FP8 | 0.510 | |
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For long documents, we take the first 2,048 tokens from the top of the document. If the document exceeds 10,000 characters, we also take the last 2,048 tokens and compute the final score as `max(top_score, bottom_score)`. |
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### Classifier Training |
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We fine-tuned a BERT-like regression model using these annotations, based on [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) for English and [jhu-clsp/mmBERT-base](https://huggingface.co/jhu-clsp/mmBERT-base) for other languages. Both models achieved the best F1 performance among the options we evaluated, while supporting FA2, which allowed us to label over 220 samples per second on an H100 GPU. |
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For each model, we unfroze both the classifier head and the last four transformer layers. To address severe class imbalance, we rebalanced the training data. |
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The resulting classifiers are available at: |
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`https://huggingface.co/HuggingFaceFW/finepdfs_edu_classifier_{lang}` |
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### Filtering and results |
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We then built 📚 FinePDFs-Edu by filtering out 90% of samples with lowest edu score for each language. Our ablation demonstrated that this refined dataset surpasses 📄 FinePDFs and all other open web datasets, with remarkable improvements on educational benchmarks such as MMLU and ARC. |
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You will find all the ablation models and datasets in [this collection](https://huggingface.co/collections/HuggingFaceFW/finepdfs). |
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## Considerations for Using the Data |
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See: [FinePDFs](https://huggingface.co/datasets/HuggingFaceFW/finepdfs). |
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## Additional Information |
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### Licensing Information |
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The dataset is released under the **Open Data Commons Attribution License (ODC-By) v1.0** [license](https://opendatacommons.org/licenses/by/1-0/). The use of this dataset is also subject to [CommonCrawl's Terms of Use](https://commoncrawl.org/terms-of-use). |
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## Citation Information |
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``` |
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@misc{kydlicek2025finepdfs, |
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title={FinePDFs}, |
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author={Hynek Kydl{\'\i}{\v{c}}ek and Guilherme Penedo and Leandro von Werra}, |
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year={2025}, |
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publisher = {Hugging Face}, |
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journal = {Hugging Face repository}, |
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howpublished = {\url{https://huggingface.co/datasets/HuggingFaceFW/finepdfs_edu}} |
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} |
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``` |
